Introduction

As per application requirements I will be analysing the data set in accordance with prescribed guidelines.

There are three major points to adhere to:

In regards to the layout of this document. The major headings in the top left will allow you to nagivate the documents. The Analysis part of this document has been nested in pages that can be accesed via tabs.

Please scroll down to the Analysis section, go through all tabs THEN scroll down to the bottom where you will find the conclusion.

This will mean a little bit of scrolling up and down when reading through the Analysis section.

To SHOW all codes chunks please find code menu button on the top right and select from the resulting drop down menu

Packages & Data

library(tidyverse)
library(kableExtra)
library(knitr)
library(Amelia)
library(ggrepel)
officer <- read_delim('test_im_officer_export_2016-03-18.csv',delim = "|")

head(officer)

## # A tibble: 6 x 8
##   company_number name  position start_date end_date occupation nationality
##   <chr>          <chr> <chr>    <lgl>      <lgl>    <lgl>      <lgl>      
## 1 005750V        Sove~ agent    NA         NA       NA         NA         
## 2 002594V        ANGL~ agent    NA         NA       NA         NA         
## 3 005820V        MIDD~ agent    NA         NA       NA         NA         
## 4 004691V        IOMA~ agent    NA         NA       NA         NA         
## 5 005187V        NORT~ agent    NA         NA       NA         NA         
## 6 003316V        Domi~ agent    NA         NA       NA         NA         
## # ... with 1 more variable: address <chr>

The data was read in using read_delim() function from readr which is a component of the tidyverse

We can see that a number of columns are missing and only have NAs present we can confirm that this is the case for the whole of the columns due to the parsing of the column as a logical as denoted by lgl.

For a quick method to see all missing values we can use the missmap function from the Amelia package.

missmap(officer)

Again, we can see that the entire set of columns are blank while the ones who do have data do not have any missing data.

To remove these columns we will use a bit of base R rather than tidyverse

officer <- officer[, !names(officer) %in% c("start_date","end_date","occupation",'nationality')]

kable(officer) %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>%
  scroll_box(width = "800px", height = "400px")